Adaptation

Richard Gomulkiewicz

Mathematical Geneology

Outline

About me
Adaptation in stochastic environments
Some of my ML projects
- Campanula range expansion (WSU)
- Fine-scale zoonotic reservoirs (UIdaho)
- Surveillance sensitivity (EHA)
Closing
Questions

About Me

Adaptation in Stochastic Environments

Bet-Hedging
- Definition: Reducing variance in fitness at the expense of lower average fitness to ensure survival across unpredictable conditions.
- Examples: Plants producing seeds with staggered germination times.
Life History Adjustments
- r-selection: High fecundity with low parental investment (e.g., many offspring).
- K-selection: Fewer offspring with higher investment (e.g., longevity, parental care).
- Dormancy: Delayed development (e.g., seed banks).

Boosting Genetic Diversity
- Role: Buffers populations against fluctuations, ensuring some individuals thrive in any condition.
- Mechanisms: Variation of mutation rate, sexual reproduction, dispersal / gene flow.
Phenotypic Plasticity
- Definition: Non-parallel reaction norms among individuals with different genotypes in response to different environmental conditions. GxE interaction.
- Examples: Seasonal coat color changes in animals; metabolic flexibility in plants.

Testing Fluctuating Selection

“The fitness of a lineage in a fluctuating environment is the time average of its fitness over the sequence of static conditions it encounters.”¹

We evolved the same barcoded library in five static conditions and five fluctuating pairs of these static conditions. We then isolated 889 mutants from the evolution experiments and performed pooled fitness assays in all environments, plus five additional fluctuating environments. B-C After correcting for mean population fitness, we estimate a mutant’s fitness from the slope of its log frequency. D The null hypothesis of additive fitness in a fluctuating environment is represented by the average of the slopes in panels B-C. Non-additive fitness might result if transitions in the fluctuating environment alter fitness of a mutant. E Non-additivity can be visualized by plotting observed fitness in a fluctuating environment vs. the null hypothesis. Mutants with non-additive fitness will appear above or below the one-to-one line.

Machine Learning

Large Language Models

Modeling Language

https://medium.com/data-science-at-microsoft/how-large-language-models-work-91c362f5b78f :::: {layout=“[[-1], [1], [-1]]”} :::: —-

1.

Abreu, C. I., Mathur, S. & Petrov, D. A. Environmental memory alters the fitness effects of adaptive mutations in fluctuating environments. Nat. Ecol. Evol. 8, 1760–1775 (2024).